Real-time predictive modelling
of epidemics
Sebastian Funk
15 March 2018
prowler.io, Cambridge
\begin{eqnarray}
\dot{S}&=&-\beta \frac{S}{N}I\\
\dot{I}&=&+\beta \frac{S}{N}I - \gamma I\\
\dot{R}&=&+\gamma I
\end{eqnarray}
What really happened
The unknown
- Community/hospital/funeral transmission
- Spatial dynamics
- Changes in behaviour
- Changes in reporting
- Interventions
- Seasonality
- etc
The known
- Average incubation period (~9 days)
- Average infectious period (~11 days)
- Case-fatality rate (~70%)
WHO Ebola response team (2014)
Transmission intensity as a stochastic process
\(d\log(\beta(t)) = \sigma dW_t\)
Dureau (2013)
Particle MCMC
- Marginalise out stochastic process
- Highly parallelisable
Andrieu (2010), Murray (2013)
Forecasting the Ebola epidemic in Western Area
Forecasting the Ebola epidemic in Western Area
Uses of real-time forecasts in outbreaks
- Plan the scale of a response or intervention
- Allocate resources (e.g., geographically)
- Plan clinical trials
Assessing forecasts
Evaluating probabilistic forecasts requires
multiple observations.
Calibration: Compatibility of forecasts and observations.
Calibration: Compatibility of forecasts and observations.
Calibration: Compatibility of forecasts and observations.
"Evaluate predictive performance on the basis of maximising the sharpness of the predictive distribution subject to calibration"
Gneiting et al., J R Stat Soc B (2007)
Sharpness
Quality of forecasts vs quality of decisions
Outlook
Forecasts are becoming part of outbreak response
Forecasting challenges
Forecasting methodology is underdeveloped
Need methods to select the best model and
combine all available data streams
(individual/spatial/genetic/social)
Louis du Plessis, University of Oxford (unpublished)
New tools
New tools
Birch
Acknowledgements
Anton Camacho, Adam Kucharski, Roz Eggo, John Edmunds (LSHTM)
Bruce Reeder, Etienne Gignoux, Iza Ciglenecki, Amanda Tiffany (MSF)
James Hensman (prowler.io), Lawrence Murray (Uppsala)
Thank you!
http://sbfnk.github.io
@sbfnk